We have previously described, in WO2009/093056, a mobile phone handset incorporating one or more acoustic sensors to pick up the unique acoustic signature of an input area struck by the user. We now describe details of some particularly preferred approaches which have been tested and found to work effectively. Background prior art can be found in US2010/085216; GB2385125; US6891527; US2011/096036; and US2003/217873, and also in patents/applications assigned to Sensitive Object, such as U.S. Pat. No. 7,511,711; U.S. Pat. No. 7,345,677; WO2006/069596; and US2005/174338.
Broadly speaking an aspect of the problem we address is to make acoustical measurements from one or more microphones embedded in a physical device. This may be an electronic device such as a mobile phone or laptop computer or a microphone or accelerometer may be attached to an inert object to make the object touch sensitive. The acoustic signal from a user tapping on the device or object using, for example, a stylus, pen tip, finger nail, or finger, is detected and analysed (either in the device or remotely) in order to determine where the object has been tapped. The resolution of this determination is variable for example the object may be provided with a grid of touch sensitive regions akin to a keyboard, or a simple differentiation may be made between, say, two regions such as one or other half of the device/object. In still other approaches the determination may simply be as to whether the device has been tapped in a particular region; or high resolution or quasi-continuous determination of a tap location may be made. In a typical application an icon may be displayed on the screen of a mobile phone in a touch-sensitive region, and user-tapping of the icon may then be employed, for example to navigate a menu. However it will be appreciated that many applications of the technology are possible.
Although the concept is simple, there are significant difficulties in achieving reliable operation. This is because the received “tap” waveforms are embedded in environmental noise such as speech, music, vehicle noise, general background noise and so forth, as well as interference. Moreover some of this noise/interference may mimic the acoustic signal from a tap. Another substantial difficulty is that the waveform measured when repeatedly tapping at the same position is not repeatable but exhibits a random variability, presumably from small differences in how the tapping is actually performed, how the device is held, and so forth.
There therefore exists a need for algorithms which can accurately and repeatably identify the region of a device on which a user has tapped.